#coding=utf-8
#Inception-V3.py
import tensorflow as tf
from datetime import datetime
import math
import time
slim = tf.contrib.slim
#产生截断的正太分布
trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)
#定义inception_v3_arg_scope，用来生成网络中经常用到的函数的默认参数
def inception_v3_arg_scope(weight_decay=0.00004, stddev=0.1, batch_norm_var_collection='moving_vars'):
    batch_norm_params = {
        'decay': 0.9997,
        'epsilon': 0.001,
        'updates_collections': tf.GraphKeys.UPDATE_OPS,
        'variables_collections':{
            'beta' : None,
            'gamma' : None,
            'moving_mean': [batch_norm_var_collection],
            'moving_variance': [batch_norm_var_collection]
        }
    }
    with slim.arg_scope([slim.conv2d, slim.fully_connected], weights_regularizer=slim.l2_regularizer(weight_decay)):
        with slim.arg_scope([slim.conv2d], weights_initializer=tf.truncated_normal_initializer(stddev=stddev),
                            activation_fn=tf.nn.relu,
                            normalizer_fn=slim.batch_norm,
                            normalizer_params=batch_norm_params) as sc:
            return sc
#定义inception_v3_base生成Inception V3网络的卷积部分（Inception Module之前的卷积池化层）
def inception_v3_base(inputs, scope=None):
    end_points = {}
    with tf.variable_scope(scope, 'InceptionV3', [inputs]):
        with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], stride=1, padding='VALID'):
            net = slim.conv2d(inputs, 32, [3, 3], stride=2, scope='Conv2d_1a_3x3')
            net = slim.conv2d(net, 32, [3, 3], scope='Conv2d_2a_3x3')
            net = slim.conv2d(net, 64, [3, 3], padding='SAME', scope='Conv2d_2b_3x3')
            net = slim.max_pool2d(net, [3, 3], stride=2, scope='MaxPool_3a_3x3')
            net = slim.conv2d(net, 80, [1, 1], scope='Conv2d_3b_1x1')
            net = slim.conv2d(net, 192, [3, 3], scope='Conv2d_4a_3x3')
            net = slim.max_pool2d(net, [3, 3], stride=2, scope='MaxPool_5a_3x3')
        #Inception Module
        with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], stride=1, padding='SAME'):
            # 第一个Inception Module的第一个模块
            with tf.variable_scope('Mixed_5b'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv2d_0b_5x5')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv2d_0b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
            # 第一个Inception Module的第2个模块
            with tf.variable_scope('Mixed_5c'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0b_1x1')
                    branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv2d_0c_5x5')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_1x1')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
            #第一个Inception Module的第3个模块
            with tf.variable_scope('Mixed_5d'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv2d_0b_5x5')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
                    branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
            #第二个Inception Module的第一个模块
            with tf.variable_scope('Mixed_6a'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 384, [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 96, [3, 3], scope='Conv2d_1a_1x1')
                    branch_1 = slim.conv2d(branch_1, 96, [3, 3], stride=2, padding='VALID', scope='Conv2d_1b_1x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='MaxPool_1a_3x3')
                net = tf.concat([branch_0, branch_1, branch_2], 3)
            #第二个Inception Module的第二个模块
            with tf.variable_scope('Mixed_6b'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 128, [1, 7], scope='Conv2d_0b_1x7')
                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 128, [7, 1], scope='Conv2d_0b_7x1')
                    branch_2 = slim.conv2d(branch_2, 128, [1, 7], scope='Conv2d_0c_1x7')
                    branch_2 = slim.conv2d(branch_2, 128, [7, 1], scope='Conv2d_0d_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
            #第二个Inception Module的第三个模块
            with tf.variable_scope('Mixed_6c'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 160, [1, 7], scope='Conv2d_0b_1x7')
                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0b_7x1')
                    branch_2 = slim.conv2d(branch_2, 160, [1, 7], scope='Conv2d_0c_1x7')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0d_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
            #第而个Inception Module的第四个模块
            with tf.variable_scope('Mixed_6d'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 160, [1, 7], scope='Conv2d_0b_1x7')
                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0b_7x1')
                    branch_2 = slim.conv2d(branch_2, 160, [1, 7], scope='Conv2d_0c_1x7')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0d_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
            #第二个Inception Module的第五个模块，且需要把Mixed_6e存储在end_points中，用于辅助模型的分类
            with tf.variable_scope('Mixed_6e'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 160, [1, 7], scope='Conv2d_0b_1x7')
                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0b_7x1')
                    branch_2 = slim.conv2d(branch_2, 160, [1, 7], scope='Conv2d_0c_1x7')
                    branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0d_7x1')
                    branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
            end_points['Mixed_6e'] = net
            #第3个Inception Module的第一个模块
            with tf.variable_scope('Mixed_7a'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                    branch_0 = slim.conv2d(branch_0, 320, [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_3x3')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = slim.conv2d(branch_1, 192, [1, 7], scope='Conv2d_0b_1x7')
                    branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
                    branch_1 = slim.conv2d(branch_1, 192, [3, 3], stride=2, padding='VALID', scope='Conv2d_1a_3x3')
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='MaxPool_1a_3x3')
                net = tf.concat([branch_0, branch_1, branch_2], 3)
            #第3个Inception Module的第二个模块
            with tf.variable_scope('Mixed_7b'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 320, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = tf.concat([slim.conv2d(branch_1, 384, [1, 3], scope='Conv2d_0b_1x3'),
                                          slim.conv2d(branch_1, 384, [3, 1], scope='Conv2d_0b_3x1')], 3)
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 448, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 384, [3, 3], scope='Conv2d_0b_3x3')
                    branch_2 = tf.concat([slim.conv2d(branch_2, 384, [1, 3], scope='Conv2d_0c_1x3'),
                                          slim.conv2d(branch_2, 384, [3, 1], scope='Conv2d_0d_3x1')], 3)

                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
            #第3个Inception Module的地三个模块
            with tf.variable_scope('Mixed_7c'):
                with tf.variable_scope('Branch_0'):
                    branch_0 = slim.conv2d(net, 320, [1, 1], scope='Conv2d_0a_1x1')
                with tf.variable_scope('Branch_1'):
                    branch_1 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1')
                    branch_1 = tf.concat([slim.conv2d(branch_1, 384, [1, 3], scope='Conv2d_0b_1x3'),
                                          slim.conv2d(branch_1, 384, [3, 1], scope='Conv2d_0b_3x1')], 3)
                with tf.variable_scope('Branch_2'):
                    branch_2 = slim.conv2d(net, 448, [1, 1], scope='Conv2d_0a_1x1')
                    branch_2 = slim.conv2d(branch_2, 384, [3, 3], scope='Conv2d_0b_3x3')
                    branch_2 = tf.concat([slim.conv2d(branch_2, 384, [1, 3], scope='Conv2d_0c_1x3'),
                                          slim.conv2d(branch_2, 384, [3, 1], scope='Conv2d_0d_3x1')], 3)

                with tf.variable_scope('Branch_3'):
                    branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
                    branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
                net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
            return net, end_points
#定义一个inception_v3函数，实现全局平均池化，Softmax和Auxiliary Logits.
def inception_v3(inputs, num_classes=1000, is_training=True, dropout_keep_prob=0.8, prediction_fn=slim.softmax,
                 sptial_squeeze=True, reuse=None, scope='InceptionV3'):
    with tf.variable_scope(scope, 'InceptionV3', [inputs, num_classes], reuse=reuse) as scope:
        with slim.arg_scope([slim.batch_norm, slim.dropout], is_training=is_training):
            net, end_points = inception_v3_base(inputs, scope=scope)
    #实现Auxiliary Logits这部分的逻辑
    with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], stride=1, padding='SAME'):
        aux_logits = end_points['Mixed_6e']
        with tf.variable_scope('AuxLogits'):
            aux_logits = slim.avg_pool2d(aux_logits, [5, 5], stride=3, padding='VALID', scope='AvgPool_1a_5x5')
            aux_logits = slim.conv2d(aux_logits, 128, [1, 1], scope='Conv2d_1b_1x1')
            aux_logits = slim.conv2d(aux_logits, 768, [5, 5], weights_initializer=trunc_normal(0.01),
                                     padding='VALID', scope='Conv2d_2a_5x5')
            aux_logits = slim.conv2d(aux_logits, num_classes, [1, 1], activation_fn=None, normalizer_fn=None,
                                     weights_initializer=trunc_normal(0.001), scope='Conv2d_2b_1x1')
            if sptial_squeeze:
                aux_logits = tf.squeeze(aux_logits, [1, 2], name='SpatialSqueeze')
            end_points['Aux:ogits'] = aux_logits
    #实现正常分类预测的逻辑
    with tf.variable_scope('Logits'):
        net = slim.avg_pool2d(net, [8, 8], padding='VALID', scope='AvgPool_1a_8x8')
        net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
        end_points['PreLogits'] = net
        logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None, normalizer_fn=None, scope='Conv2d_1c_1x1')
        if sptial_squeeze:
            logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
        end_points['Logits'] = logits
        end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
    return logits, end_points

#定义AlexNet的每轮时间评估函数
def time_tensorflow_run(session, target, info_string):
    num_steps_burn_in = 10 #程序预热
    total_durations = 0.0
    total_duration_squared = 0.0
    for i in range(num_batches + num_steps_burn_in):
        start_time = time.time()
        _ = session.run(target)
        duration = time.time() - start_time
        if i >= num_steps_burn_in:
            if not i % 10:
                print('%s: step %d, duration = %.3f'%(datetime.now(), i - num_steps_burn_in, duration))
            total_durations += duration
            total_duration_squared += duration * duration
    #计算每轮迭代的平均耗时和标准差sd,最后将结果显示出来
    mn = total_durations / num_batches
    vr = total_duration_squared / num_batches - mn * mn
    sd = math.sqrt(vr)
    print('%s: %s across %d steps, %.3f +/- %.3f sec / batch'%(datetime.now(), info_string, num_batches, mn, sd))
#对Inception-V3 进行运算性能测试
batch_size = 32
height, width = 299, 299
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope(inception_v3_arg_scope()):
    logits, end_points = inception_v3(inputs, is_training=False)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
num_batches = 100
time_tensorflow_run(sess, logits, "Foward")

